Development and External Validation of a Nomogram for Predicting Pain Risk Post-Transperineal Prostate Biopsy Using Multi-Parameter Analysis

Yang Xu,1,* Xin Li,2,* Yang Zhang,1 Peng Xue1 1Department of Urology, The Affiliated Lianyungang Hospital of Xuzhou Medical University/The First People’s Hospital of Lianyungang, Lianyungang, Jiangsu, 222000, People’s Republic of China; 2Department of Urology, Zhengzhou University Affiliated Zhengzhou Central Hospital, Zhengzhou, HeNan, 450000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Peng Xue, Department of Urology, The Affiliated Lianyungang Hospital of Xuzhou Medical University/The First People’s Hospital of Lianyungang, Lianyungang, Jiangsu, 222000, People’s Republic of China, Tel +86-0515-85605248, Email [email protected]: To explore factors associated with moderate-to-severe pain (NRS > 4) in patients undergoing ultrasound-guided transperineal prostate biopsy (TPB), and to establish and validate a nomogram model for risk assessment.Patients and Methods: This study included 520 patients who underwent ultrasound-guided TPB at the First People’s Hospital of Lianyungang City from September 2022 to December 2024. A training group (n = 400) and a validation group (n = 120) were established based on the admission time. Data collection included demographics, admission comorbidities, laboratory tests, imaging examinations, biopsy data, anxiety scores, and pain scores. Binary logistic regression was used to identify factors influencing moderate-to-severe pain (NRS > 4). A nomogram-based risk assessment model was constructed, with a validation model created to verify the training group. Additionally, a web-based dynamic nomogram risk assessment model was developed, and 120 patients from external hospitals were included for external validation.Results: Univariate analysis identified factors with statistical significance. Based on binary Logistic regression analysis, prostate volume, anxiety score, history of diabetes, biopsy time, and number of biopsy needles were risk factors, while age was a protective factor (P < 0.05). The nomogram-based risk assessment model demonstrated favorable predictive accuracy, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.940 [95% CI: 0.914– 0.967] in the training group and 0.893 [95% CI: 0.834– 0.951] in the internal validation group. External validation further confirmed robust predictive capability (AUC = 0.888 [95% CI: 0.825– 0.951]). Additionally, decision curve analysis indicated clinically meaningful net benefits.Conclusion: This nomogram-based risk stratification tool offers a robust framework for personalized perioperative pain management in patients undergoing TPB. Furthermore, external validation further supports the model’s applicability.Keywords: biopsy, prostate tumor, cancer, pain, nomogram